Translating microarray data for diagnostic testing in childhood leukaemia.
Translating microarray data for diagnostic testing in childhood leukaemia.
复制标题
在儿童白血病中翻译微阵列数据以进行诊断测试。
DOI：
10.1186/1471-2407-6-229
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发表时间：
2006-09-26
期刊：
影响因子：
3.8
通讯作者：
Kees, Ursula R.
中科院分区：
文献类型：
--
作者：
Hoffmann, Katrin;Firth, Martin J.;Beesley, Alex H.;de Klerk, Nicholas H.;Kees, Ursula R.
最近的研究结果从微阵列提出了一个标准化的诊断基因表达平台，以提高儿童急性淋巴细胞白血病（ALL）的准确诊断和风险分层的前景。然而，这种诊断测试的鲁棒性和格式仍有待确定。作为这些发现的临床应用的一步，我们使用稳健多阵列分析（RMA）和随机森林（RF）系统地分析了已发表的ALL微阵列数据集。我们研究了来自104例ALL患者标本的已发表的微阵列数据，这些标本代表了由细胞遗传学特征和免疫表型定义的6个不同亚组。使用基于决策树的监督学习算法随机森林（RF），我们确定了一小部分基因用于最佳亚组区分，随后在独立的患者队列中验证了它们的预测能力。我们实现了约98%的非常高的总体ALL亚组预测准确性，并且能够在从不同机构获得并在不同实验室处理的68份标本的独立小组中验证这些基因的稳健性。我们的研究表明，鉴别基因的选择在很大程度上取决于分析方法。这可能对临床应用有深远的影响，特别是当分类器减少到一小组基因时。我们已经证明，只有26个基因产生准确的类别预测，重要的是，这些基因中几乎70%以前没有被确定为6个ALL亚组的类别区分所必需的。我们的研究结果支持qRT-PCR技术用于儿科ALL标准化诊断检测的可行性，并应与常规细胞遗传学相结合，导致更准确的疾病分类。此外，我们已经证明，一项研究的微阵列发现可以在一项独立的研究中得到证实，使用完全独立的患者队列，并由不同的研究团队进行微阵列实验。
Recent findings from microarray studies have raised the prospect of a standardized diagnostic gene expression platform to enhance accurate diagnosis and risk stratification in paediatric acute lymphoblastic leukaemia (ALL). However, the robustness as well as the format for such a diagnostic test remains to be determined. As a step towards clinical application of these findings, we have systematically analyzed a published ALL microarray data set using Robust Multi-array Analysis (RMA) and Random Forest (RF). We examined published microarray data from 104 ALL patients specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent patient cohort. We achieved very high overall ALL subgroup prediction accuracies of about 98%, and were able to verify the robustness of these genes in an independent panel of 68 specimens obtained from a different institution and processed in a different laboratory. Our study established that the selection of discriminating genes is strongly dependent on the analysis method. This may have profound implications for clinical use, particularly when the classifier is reduced to a small set of genes. We have demonstrated that as few as 26 genes yield accurate class prediction and importantly, almost 70% of these genes have not been previously identified as essential for class distinction of the six ALL subgroups. Our finding supports the feasibility of qRT-PCR technology for standardized diagnostic testing in paediatric ALL and should, in conjunction with conventional cytogenetics lead to a more accurate classification of the disease. In addition, we have demonstrated that microarray findings from one study can be confirmed in an independent study, using an entirely independent patient cohort and with microarray experiments being performed by a different research team.
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